Gradient-based optimal control of the non-Hermitian skin effect in optomechanical arrays
- URL: http://arxiv.org/abs/2609.04492v1
- Date: Thu, 03 Sep 2026 21:25:02 GMT
- Title: Gradient-based optimal control of the non-Hermitian skin effect in optomechanical arrays
- Abstract summary: Single-port non-Hermitian sensors impose a strict resource bound on metrological precision.<n>We test whether multi-port geometry can evade this resource bound by a double-chain opto ladder with opposing non-reciprocal amplification.<n>All solutions remain Hurwitz-stable under ty5percent disorder (qty87.5percent recovery), and the deep-stability advantage survives realistic noise.<n>The architecture enables subattonewton force sensing searches across the qtyrange110gigahertz band.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In single-port non-Hermitian sensors the Petermann factor offsets susceptibility gains, imposing a strict resource bound on metrological precision. We test whether a multi-port geometry can evade this bound: a double-chain optomechanical ladder with opposing non-reciprocal hoppings spatially separates signal amplification from quantum-noise drainage, and gradient-based differentiable optimal control (DOC) maximizes the resource-normalized Fisher information $\Fnorm$ subject to a Hurwitz-stability constraint. Across system sizes $N\in\{\num{6},\dots,\num{16}\}$ the optimizer returns $\Fnorm>0$ in every case, with two coexisting solution classes whose selection is initialization-dependent: deep-stability configurations achieve $\Fnorm\in\numrange{0.937}{0.987}$ with attenuated transmission, while marginal-stability configurations deliver directional gain $\Gfwd\in\qtyrange{13.5}{15.5}{\dB}$ with isolation $\Iso\in\qtyrange{40}{64}{\dB}$. A multi-restart ensemble reveals these classes are the endpoints of a precision--gain frontier. All solutions remain Hurwitz-stable under \qty{5}{\percent} disorder (\qty{87.5}{\percent} recovery), and the deep-stability advantage survives realistic preamplifier noise at $\Fnormeff\approx\num{0.3}$--$\num{0.5}$. Mapped onto circuit-QED parameters, the architecture enables sub-attonewton force sensing and broadband axion searches across the \qtyrange{1}{10}{\giga\hertz} band.
Related papers
- Model-level synthetic-flux control of hyperchaos order and matched-resource sensing in dissipative optomechanics [0.11545092788508222]
We present a synthetic-flux phase $_rm syn$ control coordinate that selects the emphorder of a drive- and coupling-gated hyperchaos transition.<n>A phase-consistent Floquet--Lyapunov protocol (cross-checked by monodromy multipliers, dissipative volume balance $sum_i=nameTr(J)=-1.04$ and a 180-run three-seed audit) localizes the onset to a Neimark--Sacker bifurcation at $E*=num1.060
arXiv Detail & Related papers (2026-09-02T17:13:43Z) - Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise [55.29259818039367]
We study online convex optimization (OCO) in non-stationary environments under heavy-tailed noise.<n>We propose textbfHT-PAder, a parameter-free algorithm combining restarted AdaGrad experts over a geometric pool of block lengths.<n>Even in the special case of finite variance ($p=2$), HT-PAder provides the first parameter-free minimax universal dynamic regret guarantee.
arXiv Detail & Related papers (2026-07-29T15:58:18Z) - Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise [49.730496294398726]
We develop a novel quantum mean estimator for heavy-tailed random variables.<n>We derive stronger lower bounds for tail index>4/3$, showing that a nontrivial dependence on a dimension is unavoidable.<n>For a convex objective function, we propose a quantum projected gradient descent method.
arXiv Detail & Related papers (2026-07-28T09:29:37Z) - Optimal Quantum Differential Privacy via Fisher Information Spectral Analysis [0.0]
Quantum Fisher Information (QFI) metric governs a fundamental duality.<n>We exploit this duality to establish a geometry-aware framework for quantum differential privacy.
arXiv Detail & Related papers (2026-05-22T19:36:42Z) - Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability [0.0]
We identify the wavelet scaling exponent $$ as a critical diagnostic.<n>We establish $= 1/2$ as a sharp transition boundary for the classical simulability of amplitude-encoded quantum kernels.<n>We prove this variance scales strictly as $Var[X] = (d-2)$.
arXiv Detail & Related papers (2026-05-12T05:41:12Z) - Sequential Minimal Optimization for $\varepsilon$-SVR with MAPE Loss and Sample-Dependent Box Constraints [0.0]
We derive a Sequential Minimal Optimization (SMO) algorithm for the quadratic dual problem arising from $varepsilon$-SVRciteVapnik1995, Drucker1997, Smola2004 modified to minimize the Mean Absolute Percentage Error (MAPE)citeMakridakis1993, Hyndman2006.<n>An implementation is available in the open-source textttpsvr R packageciteBenavidesHerrera2026Rpsvr.
arXiv Detail & Related papers (2026-05-02T13:51:46Z) - Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning [61.07540493350384]
Self-distillation (SD) is the process of retraining a student on a mixture of ground-truth and the teacher's own predictions.<n>We show that for any prediction risk, the optimally mixed student improves upon the ridge teacher for every regularization level.<n>We propose a consistent one-shot tuning method to estimate $star$ without grid search, sample splitting, or refitting.
arXiv Detail & Related papers (2026-02-19T17:21:15Z) - Probe-then-Commit Multi-Objective Bandits: Theoretical Benefits of Limited Multi-Arm Feedback [2.1772197319352498]
We study an online resource-selection problem motivated by multi-radio access selection and mobile edge computing offloading.<n>In each round, an agent chooses among $K$ candidate links/servers whose performance is $d$-dimensional vector.<n>We develop textscPtC-P-UCB, an optimistic probe-then-commit algorithm.
arXiv Detail & Related papers (2026-02-03T06:44:00Z) - Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency [52.60557300927007]
We present a $textbfMA-OSMA$ algorithm to transfer the discrete submodular problem into a continuous optimization.<n>We also introduce a projection-free $textbfMA-OSEA$ algorithm, which effectively utilizes the KL divergence by mixing a uniform distribution.<n>Our algorithms significantly improve the $(frac11+c)$-approximation provided by the state-of-the-art OSG algorithm.
arXiv Detail & Related papers (2025-02-07T15:57:56Z) - Settling the Sample Complexity of Model-Based Offline Reinforcement
Learning [50.5790774201146]
offline reinforcement learning (RL) learns using pre-collected data without further exploration.
Prior algorithms or analyses either suffer from suboptimal sample complexities or incur high burn-in cost to reach sample optimality.
We demonstrate that the model-based (or "plug-in") approach achieves minimax-optimal sample complexity without burn-in cost.
arXiv Detail & Related papers (2022-04-11T17:26:19Z) - Naive Exploration is Optimal for Online LQR [49.681825576239355]
We show that the optimal regret scales as $widetildeTheta(sqrtd_mathbfu2 d_mathbfx T)$, where $T$ is the number of time steps, $d_mathbfu$ is the dimension of the input space, and $d_mathbfx$ is the dimension of the system state.
Our lower bounds rule out the possibility of a $mathrmpoly(logT)$-regret algorithm, which had been
arXiv Detail & Related papers (2020-01-27T03:44:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.